ant Analysis (Smoker Edition) Final

8/8/2019 ant Analysis (Smoker Edition) Final

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Definitiony A procedure for the determination of the group to which an

individual belongs, based on the characteristics of that individual.

Suppose we have measurements on p characteristics for each of a

sample of individuals. We know that each individual belongs to oneof g groups, but we do not know which. Discriminant analysis

attempts to maximize the probability of correct allocation. It differs

from cluster analysis in that we have an initial data set, the training

set, whose group allocations are known.


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Exampley Literally, "discrimination" means recognizing a difference between

two things. If I choose to buy New model Toyota car instead of

recondition car, that's an example of discrimination on the basis of

performance.


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SSPS Requirementy The observations are a random sample

y Each predictor variable is normally distributed

y Dependent variable should be categorical or nominal

y Sample size should be > 30

y There must be at least two groups or categories,

y Independent variable may have any type of measurement i.e.(ordinal, nominal, scale)


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Objectivey We take smoke is a nominal variable indicating whether the

employee smoked or not. The other variables to be used are age,

days absent sick from work last year, self-concept score, anxiety

score and attitudes to anti-smoking at work score. The aim of theanalysis is to determine whether these variables will discriminate

between those who smoke and those who do not.


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Boxs M test results tableHo : Covariance are same

H1 : Covariance are not same

Boxs M 176.474

F Approx. 11,615

df1 15

df2 600825.3

Sig. 0


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Wilks lambday Wilks lambda indicates the significance of the discriminant function.

The below table indicates a highly significant function (p < .000).

Test of

function(s)

Wilks

Lambda Chi-square df Sig.

. .


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Eigenvalues tabley This table indicates that 80.2% relationship between actual and

predicted value

Eigenvalues

Function Eigenvalue % of variance

Cumulative

%

Canonical

correlation

1 1.806a 100 100 0.802


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CanonicalDiscriminant Function

Coefficientsy D = (.024 age) + (.080 self-concept) + (.100 anxiety) + (.012

days absent) +(.134 anti smoking score) 4.543

Function1

Age .024

Self concept score .080

Anxiety score -.100

Days absent last year -.012

Total anti-smoking policies subtest .134

(Constant) -4.543


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Classification Results(a)

y 91.8% of original grouped cases correctly classified.

smoke or

not

Predicted Group

Membership Total

non-

smoker

smoker

Original Count Non smoker 238 19 257Smoker 17 164 181

% Non smoker 92.6 7.4 100.0

Smoker 9.4 90.6 100.0


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Research PaperWe would like to predict a user of Internet banking from a non-user ofInternet banking. In this case, the dependent variable is a nominal variablewith 2 levels or categories with say 1 = User and 2 =Non-user. In this case,regression analysis is no longer appropriate. Next, we have a choice ofusing a discriminant analysis which is a parametric analysis or a logisticregression analysis which is a non-parametric analysis. The basicassumption for a discriminant analysis is that the sample comes from anormally distributed population

Whereas logistic regression is called a distribution free test where thenormality requirement is not needed. This paper will only delve into theuse of discriminant analysis as parametric tests that are much morepowerful than its non-parametric alternative

(Ramayah et al., 2004;Ramayah et al., 2006).


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Thank you

ant Analysis (Smoker Edition) Final

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